{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-08T07:25:07.203051Z",
     "start_time": "2025-05-08T07:25:07.171384Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#全连接神经网络\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# 定义全连接神经网络模型\n",
    "def create_mlp(input_shape, output_units):\n",
    "    model = Sequential([\n",
    "        Dense(64, activation='relu', input_shape=(input_shape,)),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(output_units, activation='softmax')\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "# 示例：用于分类问题的MLP\n",
    "input_shape = 10  # 输入特征的维度\n",
    "output_units = 3  # 输出类别数\n",
    "\n",
    "mlp_model = create_mlp(input_shape, output_units)\n",
    "mlp_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# 打印模型结构\n",
    "mlp_model.summary()\n"
   ],
   "id": "41efc93cd7fadeb1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential_3\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_6 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │           \u001B[38;5;34m704\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │         \u001B[38;5;34m4,160\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_8 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m3\u001B[0m)              │           \u001B[38;5;34m195\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">704</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m5,059\u001B[0m (19.76 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">5,059</span> (19.76 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m5,059\u001B[0m (19.76 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">5,059</span> (19.76 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-08T07:25:07.252870Z",
     "start_time": "2025-05-08T07:25:07.227057Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#递归神经网络\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import SimpleRNN, Dense\n",
    "\n",
    "# 定义递归神经网络模型\n",
    "def create_rnn(input_shape, output_units):\n",
    "    model = Sequential([\n",
    "        SimpleRNN(64, activation='relu', input_shape=(input_shape, 1)),\n",
    "        Dense(output_units, activation='softmax')\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "# 示例：用于序列分类问题的RNN\n",
    "input_shape = 10  # 序列长度\n",
    "output_units = 3  # 输出类别数\n",
    "\n",
    "rnn_model = create_rnn(input_shape, output_units)\n",
    "rnn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# 打印模型结构\n",
    "rnn_model.summary()\n"
   ],
   "id": "49b197d48c022981",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential_4\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ simple_rnn_1 (\u001B[38;5;33mSimpleRNN\u001B[0m)        │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m64\u001B[0m)             │         \u001B[38;5;34m4,224\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m3\u001B[0m)              │           \u001B[38;5;34m195\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ simple_rnn_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,224</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">195</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m4,419\u001B[0m (17.26 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,419</span> (17.26 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m4,419\u001B[0m (17.26 KB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,419</span> (17.26 KB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-08T07:25:07.303743Z",
     "start_time": "2025-05-08T07:25:07.267382Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#卷积神经网络\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "\n",
    "# 定义卷积神经网络模型\n",
    "def create_cnn(input_shape, output_units):\n",
    "    model = Sequential([\n",
    "        Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(input_shape)),\n",
    "        MaxPooling2D(pool_size=(2, 2)),\n",
    "        Conv2D(64, kernel_size=(3, 3), activation='relu'),\n",
    "        MaxPooling2D(pool_size=(2, 2)),\n",
    "        Flatten(),\n",
    "        Dense(128, activation='relu'),\n",
    "        Dense(output_units, activation='softmax')\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "# 示例：用于图像分类问题的CNN\n",
    "input_shape = (64, 64, 3)  # 输入图像的尺寸和通道数\n",
    "output_units = 10  # 输出类别数\n",
    "\n",
    "cnn_model = create_cnn(input_shape, output_units)\n",
    "cnn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# 打印模型结构\n",
    "cnn_model.summary()\n"
   ],
   "id": "4646de43c8df3876",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential_5\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (\u001B[38;5;33mConv2D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m62\u001B[0m, \u001B[38;5;34m62\u001B[0m, \u001B[38;5;34m32\u001B[0m)     │           \u001B[38;5;34m896\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001B[38;5;33mMaxPooling2D\u001B[0m)  │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m31\u001B[0m, \u001B[38;5;34m31\u001B[0m, \u001B[38;5;34m32\u001B[0m)     │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001B[38;5;33mConv2D\u001B[0m)               │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m29\u001B[0m, \u001B[38;5;34m29\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │        \u001B[38;5;34m18,496\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001B[38;5;33mMaxPooling2D\u001B[0m)  │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m14\u001B[0m, \u001B[38;5;34m14\u001B[0m, \u001B[38;5;34m64\u001B[0m)     │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (\u001B[38;5;33mFlatten\u001B[0m)             │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m12544\u001B[0m)          │             \u001B[38;5;34m0\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m128\u001B[0m)            │     \u001B[38;5;34m1,605,760\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (\u001B[38;5;33mDense\u001B[0m)                │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m10\u001B[0m)             │         \u001B[38;5;34m1,290\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">62</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">31</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,605,760</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m1,626,442\u001B[0m (6.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,626,442</span> (6.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m1,626,442\u001B[0m (6.20 MB)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,626,442</span> (6.20 MB)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 15
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
